neuronal circuits

Balancing Time and Space in the Brain: A New Model Holds Promise for Predicting Brain Dynamics

For as long as scientists have been listening in on the activity of the brain, they have been trying to understand the source of its noisy, apparently random, activity. In the past 20 years, “balanced network theory” has emerged to explain this apparent randomness through a balance of excitation and inhibition in recurrently coupled networks of neurons. A team of scientists has extended the balanced model to provide deep and testable predictions linking brain circuits to brain activity.

Lead investigators at the University of Pittsburgh say the new model accurately explains experimental findings about the highly variable responses of neurons in the brains of living animals. On Oct. 31, their paper, “The spatial structure of correlated neuronal variability,” was published online by the journal Nature Neuroscience.

The new model provides a much richer understanding of how activity is coordinated between neurons in neural circuits. The model could be used in the future to discover neural “signatures” that predict brain activity associated with learning or disease, say the investigators.

“Normally, brain activity appears highly random and variable most of the time, which looks like a weird way to compute,” said Brent Doiron, associate professor of mathematics at Pitt, senior author on the paper, and a member of the University of Pittsburgh Brain Institute (UPBI). “To understand the mechanics of neural computation, you need to know how the dynamics of a neuronal network depends on the network’s architecture, and this latest research brings us significantly closer to achieving this goal.”

Earlier versions of the balanced network theory captured how the timing and frequency of inputs—excitatory and inhibitory—shaped the emergence of variability in neural behavior, but these models used shortcuts that were biologically unrealistic, according to Doiron.

“The original balanced model ignored the spatial dependence of wiring in the brain, but it has long been known that neuron pairs that are near one another have a higher likelihood of connecting than pairs that are separated by larger distances. Earlier models produced unrealistic behavior—either completely random activity that was unlike the brain or completely synchronized neural behavior, such as you would see in a deep seizure. You could produce nothing in between.”

In the context of this balance, neurons are in a constant state of tension. According to co-author Matthew Smith, assistant professor of ophthalmology at Pitt and a member of UPBI, “It’s like balancing on one foot on your toes. If there are small overcorrections, the result is big fluctuations in neural firing, or communication.”

The new model accounts for temporal and spatial characteristics of neural networks and the correlations in the activity between neurons—whether firing in one neuron is correlated with firing in another. The model is such a substantial improvement that the scientists could use it to predict the behavior of living neurons examined in the area of the brain that processes the visual world.

After developing the model, the scientists examined data from the living visual cortex and found that their model accurately predicted the behavior of neurons based on how far apart they were. The activity of nearby neuron pairs was strongly correlated. At an intermediate distance, pairs of neurons were anticorrelated (When one responded more, the other responded less.), and at greater distances still they were independent.

“This model will help us to better understand how the brain computes information because it’s a big step forward in describing how network structure determines network variability,” said Doiron. “Any serious theory of brain computation must take into account the noise in the code. A shift in neuronal variability accompanies important cognitive functions, such as attention and learning, as well as being a signature of devastating pathologies like Parkinson’s disease and epilepsy.”

While the scientists examined the visual cortex, they believe their model could be used to predict activity in other parts of the brain, such as areas that process auditory or olfactory cues, for example. And they believe that the model generalizes to the brains of all mammals. In fact, the team found that a neural signature predicted by their model appeared in the visual cortex of living mice studied by another team of investigators.

“A hallmark of the computational approach that Doiron and Smith are taking is that its goal is to infer general principles of brain function that can be broadly applied to many scenarios. Remarkably, we still don’t have things like the laws of gravity for understanding the brain, but this is an important step for providing good theories in neuroscience that will allow us to make sense of the explosion of new experimental data that can now be collected,” said Nathan Urban, associate director of UPBI.

Scientists Outline How Brain Separates Relevant & Irrelevant Information

Imagine yourself sitting in a noisy café trying to read. To focus on the book at hand, you need to ignore the surrounding chatter and clattering of cups, with your brain filtering out the irrelevant stimuli coming through your ears and “gating” in the relevant ones in your vision—words on a page.

In a new paper in the journal Nature Communications, New York University researchers offer a new theory, based on a computational model, on how the brain separates relevant from irrelevant information in these and other circumstances.

“It is critical to our everyday life that our brain processes the most important information out of everything presented to us,” explains Xiao-Jing Wang, Global Professor of Neural Science at NYU and NYU Shanghai and the paper’s senior author. “Within an extremely complicated neural circuit in the brain, there must be a gating mechanism to route relevant information to the right place at the right time.”

The analysis focuses on inhibitory neurons—the brain’s traffic cops that help ensure proper neurological responses to incoming stimuli by suppressing other neurons and working to balance excitatory neurons, which aim to stimulate neuronal activity.

“Our model uses a fundamental element of the brain circuit, involving multiple types of inhibitory neurons, to achieve this goal,” Wang adds. “Our computational model shows that inhibitory neurons can enable a neural circuit to gate in specific pathways of information while filtering out the rest.”

In their analysis, led by Guangyu Robert Yang, a doctoral candidate in Wang’s lab, the researchers devised a model that maps out a more complicated role for inhibitory neurons than had previously been suggested.

Of particular interest to the team was a specific subtype of inhibitory neurons that targets the excitatory neurons’ dendrites—components of a neuron where inputs from other neurons are located. These dendrite-targeting inhibitory neurons are labeled by a biological marker called somatostatin and can be studied selectively by experimentalists. The researchers proposed that they not only control the overall inputs to a neuron, but also the inputs from individual pathways—for example, the visual or auditory pathways converging onto a neuron.

“This was thought to be difficult because the connections from inhibitory neurons to excitatory neurons appeared dense and unstructured,” observes Yang. “Thus a surprising finding from our study is that the precision required for pathway-specific gating can be realized by inhibitory neurons.”

The study’s authors used computational models to show that even with the seemingly random connections, these dendrite-targeting neurons can gate individual pathways by aligning with excitatory inputs through different pathways. They showed that this alignment can be realized through synaptic plasticity—a brain mechanism for learning through experience.

Supporting the damaged brain

A new study shows that embryonic nerve cells can functionally integrate into local neural networks when transplanted into damaged areas of the visual cortex of adult mice.

(Image caption: Neuronal transplants (blue) connect with host neurons (yellow) in the adult mouse brain in a highly specific manner, rebuilding neural networks lost upon injury. Credit: Sofia Grade, LMU/Helmholtz Zentrum München)

When it comes to recovering from insult, the adult human brain has very little ability to compensate for nerve-cell loss. Biomedical researchers and clinicians are therefore exploring the possibility of using transplanted nerve cells to replace neurons that have been irreparably damaged as a result of trauma or disease. Previous studies have suggested there is potential to remedy at least some of the clinical symptoms resulting from acquired brain disease through the transplantation of fetal nerve cells into damaged neuronal networks. However, it is not clear whether transplanted intact neurons can be sufficiently integrated to result in restored function of the lesioned network. Now researchers based at LMU Munich, the Max Planck Institute for Neurobiology in Martinsried and the Helmholtz Zentrum München have demonstrated that, in mice, transplanted embryonic nerve cells can indeed be incorporated into an existing network in such a way that they correctly carry out the tasks performed by the damaged cells originally found in that position. Such work is of importance in the potential treatment of all acquired brain disease including neurodegenerative illnesses such as Alzheimer‘s or Parkinson’s disease, as well as strokes and trauma, given each disease state leads to the large-scale, irreversible loss of nerve cells and the acquisition of a what is usually a lifelong neurological deficit for the affected person.

In the study published in Nature, researchers of the Ludwig Maximilians University Munich, the Max Planck Institute of Neurobiology, and the Helmholtz Zentrum München have specifically asked whether transplanted embryonic nerve cells can functionally integrate into the visual cortex of adult mice. “This region of the brain is ideal for such experiments,” says Magdalena Götz, joint leader of the study together with Mark Hübener. Hübener is a specialist in the structure and function of the mouse visual cortex in Professor Tobias Bonhoeffer’s Department (Synapses – Circuits – Plasticity) at the MPI for Neurobiology. As Hübener explains, “we know so much about the functions of the nerve cells in this region and the connections between them that we can readily assess whether the implanted nerve cells actually perform the tasks normally carried out by the network.” In their experiments, the team transplanted embryonic nerve cells from the cerebral cortex into lesioned areas of the visual cortex of adult mice. Over the course of the following weeks and months, they monitored the behavior of the implanted, immature neurons by means of two-photon microscopy to ascertain whether they differentiated into so-called pyramidal cells, a cell type normally found in the area of interest. “The very fact that the cells survived and continued to develop was very encouraging,” Hübener remarks. “But things got really exciting when we took a closer look at the electrical activity of the transplanted cells.” In their joint study, PhD student Susanne Falkner and Postdoc Sofia Grade were able to show that the new cells formed the synaptic connections that neurons in their position in the network would normally make, and that they responded to visual stimuli.

The team then went on to characterize, for the first time, the broader pattern of connections made by the transplanted neurons. Astonishingly, they found that pyramidal cells derived from the transplanted immature neurons formed functional connections with the appropriate nerve cells all over the brain. In other words, they received precisely the same inputs as their predecessors in the network. In addition, they were able to process that information and pass it on to the downstream neurons which had also differentiated in the correct manner. “These findings demonstrate that the implanted nerve cells have integrated with high precision into a neuronal network into which, under normal conditions, new nerve cells would never have been incorporated,” explains Götz, whose work at the Helmholtz Zentrum and at LMU focuses on finding ways to replace lost neurons in the central nervous system. The new study reveals that immature neurons are capable of correctly responding to differentiation signals in the adult mammalian brain and can close functional gaps in an existing neural network.

Can the brain feel it? The world’s smallest extracellular needle-electrodes

A research team in the Department of Electrical and Electronic Information Engineering and the Electronics-Inspired Interdisciplinary Research Institute (EIIRIS) at Toyohashi University of Technology developed 5-μm-diameter needle-electrodes on 1 mm × 1 mm block modules. This tiny needle may help solve the mysteries of the brain and facilitate the development of a brain-machine interface. The research results were reported in Scientific Reports
on Oct 25, 2016.

(Image caption: Extracellular needle-electrode with a diameter of 5 μm mounted on a connector)

The neuron networks in the human brain are extremely complex. Microfabricated silicon needle-electrode devices were expected to be an innovation that would be able to record and analyze the electrical activities of the microscale neuronal circuits in the brain.

However, smaller needle technologies (e.g., needle diameter < 10 μm) are necessary to reduce damage to brain tissue. In addition to the needle geometry, the device substrate should be minimized not only to reduce the total amount of damage to tissue but also to enhance the accessibility of the
electrode in the brain. Thus, these electrode technologies will realize new experimental neurophysiological concepts.

A research team in the Department of Electrical and Electronic Information Engineering and the EIIRIS at Toyohashi University of Technology developed 5-
μm-diameter needle-electrodes on 1 mm × 1 mm block modules.

The individual microneedles are fabricated on the block modules, which are small enough to use in the narrow spaces present in brain tissue; as demonstrated in the recording using mouse cerebrum cortices. In addition, the block module remarkably improves the design variability in the packaging, offering numerous in vivo recording applications.

“We demonstrated the high design variability in the packaging of our electrode device, and in vivo neuronal recordings were performed by simply placing the device on a mouse’s brain. We were very surprised that high quality signals of a single unit were stably recorded over a long period using the 5-μm-diameter needle,” explained the first author, Assistant Professor Hirohito Sawahata, and co-author, researcher Shota Yamagiwa.

The leader of the research team, Associate Professor Takeshi Kawano said: “Our silicon needle technology offers low invasive neuronal recordings and provides novel methodologies for electrophysiology; therefore, it has the potential to enhance experimental neuroscience.” He added, “We expect the development of applications to solve the mysteries of the brain and the development of brain–machine interfaces.”

Scientists Develop Computer Model Explaining How Brain Learns to Categorize

New York University researchers have devised a computer model to explain how a neural circuit learns to classify sensory stimuli into discrete categories, such as “car vs. motorcycle.” Their findings, which appear in the journal Nature Communication, shed new light on the brain processes underpinning judgments we make on a daily basis.

“Categorization is vital for survival, such as distinguishing food from inedible things, as well as for formation of concepts, for instance ‘dog vs. cat,’ and relationship between concepts, such as hierarchical classification of animals,” says author Xiao-Jing Wang, Global Professor of Neural Science, Physics, and Mathematics at NYU and NYU Shanghai. “Our proposed model can only explain category learning of simple visual stimuli. Future research is needed to explore if the general principles extracted from this model are applicable to more complex categorizations.”

Wang conducted the study with Tatiana Engel, a postdoctoral associate at the time of the study, and Jah Chaisangmongkon, a doctoral candidate in his group, in collaboration with experimentalist David Freedman, a neurobiologist at the University of Chicago. Freedman had previously developed a behavioral paradigm for investigating electrical activity of single-neurons that are correlated with category memberships of visual stimuli.

In this neural-circuit model, which incorporates what we know about the organization and neurophysiology of the cortex, lower-level neural circuits send information about visual stimuli to a higher-level neural circuit where an analog stimulus feature (like the direction of a random pattern of moving dots) is classified into binary categories (A or B). The researchers’ results showed that the model captured a wide range of experimental observations and yielded specific predictions that were confirmed by an analysis of single-neuron electrical activity recorded in a category-learning experiment.

Interestingly, the researchers found that learning a correct category boundary (dividing the continuous feature into A and B) requires top-down feedback projection from category-selective neurons to feature-coding neurons.

Since the pioneering work by NYU’s J. Anthony Movshon, Stanford’s William Newsome, and others, it has been well known that feature-coding sensory neurons reflect an animal’s choice about categorical membership (A or B) of a stimulus in a probabilistic way (quantified as “choice probability”). The common belief was that this is because a category choice is influenced by stochastic, or random, activity of sensory neurons through bottom-up, sensory-to-category pathways.

The new model, reported in the Nature Communications article, suggests a novel interpretation, namely that such “choice probability” results from category-to-sensory, top-down signaling.

This finding offers new insights into feedback projections in the brain whose functional significance had previously been a long-standing puzzle, the researchers note.